Furthermore, in addition to this classical perception-planning-control work, many researchers are focusing on full or partial end-to-end approaches that leverage the usage of deep neural networks (DNN) or reinforcement learning (RL) methods. The racing task provides a clear objective function (fastest lap time) for the algorithm training and the race track provides with its clear driveable area and one class of objects a perfect proving ground. Researchers in this field displayed partial end-to-end approaches [1]}, [2]} that combine DNNs with MPC methods to create and follow dynamic trajectories. In addition, by using algorithms from the field of RL (e.g., Soft-Actor-Critic, Q-Learning), researchers were able to demonstrate how to train an agent to drive fast [3]}, [4]}, how to train an agent to overtake other agents on the race track [5]} and how to bridge the sim-to-real gap with model-based RL approaches [6]}.
